Data Modeling Made Easy: A Beginner’S Guide To Data Modeling

Data Modeling Made Easy: A Beginner’S Guide To Data Modeling
Published 6/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

Learn how to design clear, structured data models using real-world examples and simple visuals-no experience needed!

What you’ll learn

Understand foundational data modeling concepts such as entities, attributes, relationships, and keys.

Differentiate between conceptual, logical, and physical data models, and understand when to use each.

Draw ER diagrams using Crow’s Foot notation to represent real-world scenarios.

Build normalized, well-structured data models that reflect business rules and avoid redundancy.

Design dimensional models (Star/Snowflake schemas) to support analytics and reporting.

Requirements

Basic understanding of what a database is will help

A curious mind and interest in solving real-world problems with data

Access to internet

You do NOT need any programming or database experience to take this course

Description

Overview

Section 1: Introduction

Lecture 1 Introduction To This Course

Section 2: Introduction to Data Modeling Fundamentals

Lecture 2 What is Data Modeling?

Lecture 3 Why Data Modeling matters (real-world examples)

Lecture 4 Overview of transactional vs. analytical data modeling
Lecture 5 Data modeling vs. database design

Section 3: Basic Data Modeling Concepts & Terminology

Lecture 6 What is an Entity, Attribute, and Relationship?

Lecture 7 Requirement For Choosing Attributes

Lecture 8 Strong vs. Weak Entities, Tables = Entities, Columns = Attributes

Lecture 9 Primary Key & Foreign Key

Lecture 10 Build Relationships Between Entities (One-to-One, One-to-Many, Many-to-Many)

Lecture 11 What Is Multi-Valued Attributes

Section 4: Building Blocks of a Data Model

Lecture 12 Identify entities and attributes

Lecture 13 Create Tables and Add Attributes

Lecture 14 Multi-Valued Attributes and How to Handle Them

Lecture 15 Summarize: how to structure a basic data model

Section 5: Understanding Relationships & Cardinality

Lecture 16 What Are Entity Relationships (ERD) in Data Modeling?

Lecture 17 What is Cardinality?

Lecture 18 max/min values explained

Lecture 19 Why Real-World Complexities Matter

Lecture 20 Build Relationships (with visuals)

Lecture 21 Chen Notations

Lecture 22 Crow’s Foot Notation Basics

Lecture 23 Complex Relationships in Practice

Section 6: Real-World Modeling: Entity & Attribute Constraints

Lecture 24 Attribute constraints (data types, required fields)

Lecture 25 Entity hierarchies (e.g., Employee -> Manager)

Lecture 26 Cross-entity dependencies (weak entities with FK reliance)

Lecture 27 Summary of modeling complex real-world scenarios

Section 7: Navigate Methodologies, Techniques, and Notations

Lecture 28 UML : Why It Matters in Data Modeling

Lecture 29 Overview of ER, UML

Lecture 30 ER, UML, Crow’s Foot notation Choosing the right technique

Lecture 31 Visual demo using dbdiagram.io or draw.io

Section 8: Working with Different Levels of a Data Model

Lecture 32 Conceptual vs Logical vs Physical Models

Lecture 33 Forward-engineering: from conceptual to physical

Lecture 34 Reverse-engineering: from database to ERD

Lecture 35 What is Normalizations?

Lecture 36 Different Types of Anomalies (Insert, Update, Delete)

Lecture 37 How to solve This Data Issues?

Lecture 38 Introductions to normalization-1NF

Lecture 39 Introductions to normalization-2NF

Lecture 40 Introductions to normalization-3NF

Section 9: Dimensional Modeling Basics (for Analytics)

Lecture 41 Star Schema vs Snowflake Schema

Lecture 42 Fact tables vs Dimension tables

Lecture 43 Use case: Sales dashboard-Visualize a simple star schema with a fact table

Section 10: Practice Data Modeling

Section 11: Bonus

Lecture 44 Bonus Lecture